Abstract LB189: Three-dimensional genome organization maps in normal haematopoietic stem cells and acute myeloid leukemia
Bibliographic record
Abstract
Abstract Many oncogenes, such as MEIS1 and HOXA9, are overexpressed in some but not all cancers. We identified two key epigenetic mechanisms underlying this heterogeneity in oncogene expression in Acute Myeloid Leukemia. Acute Myeloid Leukemia (AML) is a highly lethal blood cancer arising due to aberrant differentiation of haematopoietic stem cells. MEIS1 and HOXA9 regulate stemness-related transcriptional programs in normal haematopoietic stem cells and AML. Here we obtained 3D genome organization maps in the CD34+ haematopoietic stem cells from healthy individuals and individuals with AML. The MEIS1 oncogenic transcription factor is regulated by a Frequently Interacting Region (FIRE). This FIRE is present in normal bone marrow samples, and an AML sample with high MEIS1 levels. However, it is absent in two AML samples that show low MEIS1 levels. CRISPR excision of the FIRE led to loss of MEIS1 and reduced cell growth. Moreover, MEIS1 can bind to the promoter of HOXA9. HOXA9 can also auto-regulate by binding to its own promoter as well as an Acute Myeloid Leukemia-specific super-enhancer that interacts with the HOXA9 promoter via chromatin interactions. The mechanisms elucidated here could be potentially exploited to utilize epigenetic inhibitors to specifically target oncogene expression in cancer. Citation Format: Benny Wang, Lingshi Kong, Deepak Babu, Ruchi Choudhary, Winnie Fam, Jia Qi Tng, Yufen Goh, Xin Liu, Fang Fang Song, Priscella Chia, Ming Chun Chan, Omer An, Cheng Yong Tham, Touati Benoukraf, Henry Yang, Wilson Wang, Wee Joo Chng, Daniel Tenen, Melissa J. Fullwood. Three-dimensional genome organization maps in normal haematopoietic stem cells and acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB189.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".